Overview

Dataset statistics

Number of variables14
Number of observations27
Missing cells10
Missing cells (%)2.6%
Total size in memory3.1 KiB
Average record size in memory116.7 B

Variable types

Text12
Numeric2

Alerts

arenacapacity has 10 (37.0%) missing valuesMissing
team_id has unique valuesUnique
abbreviation has unique valuesUnique
nickname has unique valuesUnique
owner has unique valuesUnique
generalmanager has unique valuesUnique
headcoach has unique valuesUnique
facebook has unique valuesUnique
instagram has unique valuesUnique
twitter has unique valuesUnique
arenacapacity has 1 (3.7%) zerosZeros

Reproduction

Analysis started2023-07-13 14:07:05.460258
Analysis finished2023-07-13 14:07:05.621537
Duration0.16 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

team_id
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:05.758512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters270
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row1610612737
2nd row1610612738
3rd row1610612739
4th row1610612740
5th row1610612741
ValueCountFrequency (%)
1610612737 1
 
3.7%
1610612738 1
 
3.7%
1610612739 1
 
3.7%
1610612740 1
 
3.7%
1610612741 1
 
3.7%
1610612742 1
 
3.7%
1610612743 1
 
3.7%
1610612744 1
 
3.7%
1610612745 1
 
3.7%
1610612746 1
 
3.7%
Other values (17) 17
63.0%
2023-07-13T22:07:06.000976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 84
31.1%
6 61
22.6%
0 30
 
11.1%
2 30
 
11.1%
7 30
 
11.1%
5 12
 
4.4%
4 12
 
4.4%
3 6
 
2.2%
8 3
 
1.1%
9 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 84
31.1%
6 61
22.6%
0 30
 
11.1%
2 30
 
11.1%
7 30
 
11.1%
5 12
 
4.4%
4 12
 
4.4%
3 6
 
2.2%
8 3
 
1.1%
9 2
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 84
31.1%
6 61
22.6%
0 30
 
11.1%
2 30
 
11.1%
7 30
 
11.1%
5 12
 
4.4%
4 12
 
4.4%
3 6
 
2.2%
8 3
 
1.1%
9 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 84
31.1%
6 61
22.6%
0 30
 
11.1%
2 30
 
11.1%
7 30
 
11.1%
5 12
 
4.4%
4 12
 
4.4%
3 6
 
2.2%
8 3
 
1.1%
9 2
 
0.7%

abbreviation
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:06.144696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowATL
2nd rowBOS
3rd rowCLE
4th rowNOP
5th rowCHI
ValueCountFrequency (%)
atl 1
 
3.7%
bos 1
 
3.7%
cle 1
 
3.7%
nop 1
 
3.7%
chi 1
 
3.7%
dal 1
 
3.7%
den 1
 
3.7%
gsw 1
 
3.7%
hou 1
 
3.7%
lac 1
 
3.7%
Other values (17) 17
63.0%
2023-07-13T22:07:06.327355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 8
 
9.9%
A 7
 
8.6%
O 7
 
8.6%
I 6
 
7.4%
N 6
 
7.4%
M 5
 
6.2%
C 5
 
6.2%
P 4
 
4.9%
D 4
 
4.9%
E 4
 
4.9%
Other values (11) 25
30.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 8
 
9.9%
A 7
 
8.6%
O 7
 
8.6%
I 6
 
7.4%
N 6
 
7.4%
M 5
 
6.2%
C 5
 
6.2%
P 4
 
4.9%
D 4
 
4.9%
E 4
 
4.9%
Other values (11) 25
30.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 81
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 8
 
9.9%
A 7
 
8.6%
O 7
 
8.6%
I 6
 
7.4%
N 6
 
7.4%
M 5
 
6.2%
C 5
 
6.2%
P 4
 
4.9%
D 4
 
4.9%
E 4
 
4.9%
Other values (11) 25
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 8
 
9.9%
A 7
 
8.6%
O 7
 
8.6%
I 6
 
7.4%
N 6
 
7.4%
M 5
 
6.2%
C 5
 
6.2%
P 4
 
4.9%
D 4
 
4.9%
E 4
 
4.9%
Other values (11) 25
30.9%

nickname
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:06.482618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length9
Mean length6.740740741
Min length4

Characters and Unicode

Total characters182
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowHawks
2nd rowCeltics
3rd rowCavaliers
4th rowPelicans
5th rowBulls
ValueCountFrequency (%)
hawks 1
 
3.6%
nets 1
 
3.6%
cavaliers 1
 
3.6%
pelicans 1
 
3.6%
bulls 1
 
3.6%
mavericks 1
 
3.6%
nuggets 1
 
3.6%
warriors 1
 
3.6%
rockets 1
 
3.6%
clippers 1
 
3.6%
Other values (18) 18
64.3%
2023-07-13T22:07:06.683911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 24
 
13.2%
e 17
 
9.3%
r 15
 
8.2%
a 14
 
7.7%
i 14
 
7.7%
l 10
 
5.5%
c 8
 
4.4%
t 7
 
3.8%
n 6
 
3.3%
k 6
 
3.3%
Other values (28) 61
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 152
83.5%
Uppercase Letter 27
 
14.8%
Decimal Number 2
 
1.1%
Space Separator 1
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 24
15.8%
e 17
11.2%
r 15
9.9%
a 14
9.2%
i 14
9.2%
l 10
 
6.6%
c 8
 
5.3%
t 7
 
4.6%
n 6
 
3.9%
k 6
 
3.9%
Other values (11) 31
20.4%
Uppercase Letter
ValueCountFrequency (%)
P 3
11.1%
T 3
11.1%
B 3
11.1%
C 3
11.1%
K 2
7.4%
M 2
7.4%
H 2
7.4%
R 2
7.4%
N 2
7.4%
S 1
 
3.7%
Other values (4) 4
14.8%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
6 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 179
98.4%
Common 3
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 24
13.4%
e 17
 
9.5%
r 15
 
8.4%
a 14
 
7.8%
i 14
 
7.8%
l 10
 
5.6%
c 8
 
4.5%
t 7
 
3.9%
n 6
 
3.4%
k 6
 
3.4%
Other values (25) 58
32.4%
Common
ValueCountFrequency (%)
1
33.3%
7 1
33.3%
6 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 24
 
13.2%
e 17
 
9.3%
r 15
 
8.2%
a 14
 
7.7%
i 14
 
7.7%
l 10
 
5.5%
c 8
 
4.4%
t 7
 
3.8%
n 6
 
3.3%
k 6
 
3.3%
Other values (28) 61
33.5%

yearfounded
Real number (ℝ)

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1969.111111
Minimum1946
Maximum2002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-07-13T22:07:06.744039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1946
5-th percentile1946
Q11949
median1970
Q31978
95-th percentile1995
Maximum2002
Range56
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.12697677
Coefficient of variation (CV)0.008697821405
Kurtosis-0.9530193976
Mean1969.111111
Median Absolute Deviation (MAD)18
Skewness0.1121552942
Sum53166
Variance293.3333333
MonotonicityNot monotonic
2023-07-13T22:07:06.786192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1946 3
11.1%
1970 3
11.1%
1976 3
11.1%
1948 3
11.1%
1949 2
7.4%
1967 2
7.4%
1968 2
7.4%
1989 2
7.4%
1995 2
7.4%
2002 1
 
3.7%
Other values (4) 4
14.8%
ValueCountFrequency (%)
1946 3
11.1%
1948 3
11.1%
1949 2
7.4%
1966 1
 
3.7%
1967 2
7.4%
ValueCountFrequency (%)
2002 1
3.7%
1995 2
7.4%
1989 2
7.4%
1988 1
3.7%
1980 1
3.7%

city
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:06.924901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.185185185
Min length4

Characters and Unicode

Total characters221
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)92.6%

Sample

1st rowAtlanta
2nd rowBoston
3rd rowCleveland
4th rowNew Orleans
5th rowChicago
ValueCountFrequency (%)
los 2
 
6.1%
new 2
 
6.1%
angeles 2
 
6.1%
brooklyn 1
 
3.0%
cleveland 1
 
3.0%
orleans 1
 
3.0%
chicago 1
 
3.0%
dallas 1
 
3.0%
denver 1
 
3.0%
state 1
 
3.0%
Other values (20) 20
60.6%
2023-07-13T22:07:07.133437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 22
 
10.0%
o 21
 
9.5%
e 21
 
9.5%
n 19
 
8.6%
l 16
 
7.2%
t 14
 
6.3%
i 12
 
5.4%
s 10
 
4.5%
r 9
 
4.1%
h 7
 
3.2%
Other values (28) 70
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 182
82.4%
Uppercase Letter 33
 
14.9%
Space Separator 6
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22
12.1%
o 21
11.5%
e 21
11.5%
n 19
10.4%
l 16
8.8%
t 14
7.7%
i 12
 
6.6%
s 10
 
5.5%
r 9
 
4.9%
h 7
 
3.8%
Other values (11) 31
17.0%
Uppercase Letter
ValueCountFrequency (%)
M 4
12.1%
P 3
9.1%
C 3
9.1%
O 3
9.1%
A 3
9.1%
D 3
9.1%
B 2
 
6.1%
S 2
 
6.1%
L 2
 
6.1%
N 2
 
6.1%
Other values (6) 6
18.2%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 215
97.3%
Common 6
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22
 
10.2%
o 21
 
9.8%
e 21
 
9.8%
n 19
 
8.8%
l 16
 
7.4%
t 14
 
6.5%
i 12
 
5.6%
s 10
 
4.7%
r 9
 
4.2%
h 7
 
3.3%
Other values (27) 64
29.8%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22
 
10.0%
o 21
 
9.5%
e 21
 
9.5%
n 19
 
8.6%
l 16
 
7.2%
t 14
 
6.3%
i 12
 
5.4%
s 10
 
4.5%
r 9
 
4.1%
h 7
 
3.2%
Other values (28) 70
31.7%

arena
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:07.278965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length22
Mean length15.33333333
Min length9

Characters and Unicode

Total characters414
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)92.6%

Sample

1st rowState Farm Arena
2nd rowTD Garden
3rd rowRocket Mortgage FieldHouse
4th rowSmoothie King Center
5th rowUnited Center
ValueCountFrequency (%)
center 15
24.6%
arena 6
 
9.8%
crypto.com 2
 
3.3%
garden 2
 
3.3%
fieldhouse 2
 
3.3%
square 1
 
1.6%
american 1
 
1.6%
fiserv 1
 
1.6%
forum 1
 
1.6%
kaseya 1
 
1.6%
Other values (29) 29
47.5%
2023-07-13T22:07:07.482455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 61
14.7%
r 39
 
9.4%
35
 
8.5%
a 32
 
7.7%
n 32
 
7.7%
t 31
 
7.5%
o 22
 
5.3%
C 19
 
4.6%
i 15
 
3.6%
l 11
 
2.7%
Other values (33) 117
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 312
75.4%
Uppercase Letter 64
 
15.5%
Space Separator 35
 
8.5%
Other Punctuation 2
 
0.5%
Decimal Number 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 61
19.6%
r 39
12.5%
a 32
10.3%
n 32
10.3%
t 31
9.9%
o 22
 
7.1%
i 15
 
4.8%
l 11
 
3.5%
s 11
 
3.5%
d 10
 
3.2%
Other values (13) 48
15.4%
Uppercase Letter
ValueCountFrequency (%)
C 19
29.7%
A 9
14.1%
F 9
14.1%
S 4
 
6.2%
G 4
 
6.2%
M 3
 
4.7%
T 3
 
4.7%
B 2
 
3.1%
D 2
 
3.1%
K 2
 
3.1%
Other values (7) 7
 
10.9%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 376
90.8%
Common 38
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 61
16.2%
r 39
 
10.4%
a 32
 
8.5%
n 32
 
8.5%
t 31
 
8.2%
o 22
 
5.9%
C 19
 
5.1%
i 15
 
4.0%
l 11
 
2.9%
s 11
 
2.9%
Other values (30) 103
27.4%
Common
ValueCountFrequency (%)
35
92.1%
. 2
 
5.3%
1 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 61
14.7%
r 39
 
9.4%
35
 
8.5%
a 32
 
7.7%
n 32
 
7.7%
t 31
 
7.5%
o 22
 
5.3%
C 19
 
4.6%
i 15
 
3.6%
l 11
 
2.7%
Other values (33) 117
28.3%

arenacapacity
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)88.2%
Missing10
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean17953.70588
Minimum0
Maximum21711
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size344.0 B
2023-07-13T22:07:07.545612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14000
Q118119
median19060
Q319600
95-th percentile20791.8
Maximum21711
Range21711
Interquartile range (IQR)1481

Descriptive statistics

Standard deviation4749.856942
Coefficient of variation (CV)0.2645613654
Kurtosis14.98469869
Mean17953.70588
Median Absolute Deviation (MAD)715
Skewness-3.760763514
Sum305213
Variance22561140.97
MonotonicityNot monotonic
2023-07-13T22:07:07.590314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
19060 2
 
7.4%
17500 2
 
7.4%
18729 1
 
3.7%
18624 1
 
3.7%
20562 1
 
3.7%
21711 1
 
3.7%
19200 1
 
3.7%
18104 1
 
3.7%
19600 1
 
3.7%
19356 1
 
3.7%
Other values (5) 5
18.5%
(Missing) 10
37.0%
ValueCountFrequency (%)
0 1
3.7%
17500 2
7.4%
18104 1
3.7%
18119 1
3.7%
18345 1
3.7%
ValueCountFrequency (%)
21711 1
3.7%
20562 1
3.7%
19980 1
3.7%
19763 1
3.7%
19600 1
3.7%

owner
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:07.749807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length18
Mean length12.66666667
Min length8

Characters and Unicode

Total characters342
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowTony Ressler
2nd rowWyc Grousbeck
3rd rowDan Gilbert
4th rowGayle Benson
5th rowMichael Reinsdorf
ValueCountFrequency (%)
joe 2
 
3.3%
dan 2
 
3.3%
2
 
3.3%
tony 1
 
1.7%
ballmer 1
 
1.7%
grousbeck 1
 
1.7%
wesley 1
 
1.7%
edens 1
 
1.7%
marc 1
 
1.7%
lasry 1
 
1.7%
Other values (47) 47
78.3%
2023-07-13T22:07:07.982745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 36
 
10.5%
32
 
9.4%
a 30
 
8.8%
n 24
 
7.0%
r 20
 
5.8%
o 18
 
5.3%
i 18
 
5.3%
s 17
 
5.0%
l 15
 
4.4%
t 12
 
3.5%
Other values (33) 120
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 248
72.5%
Uppercase Letter 59
 
17.3%
Space Separator 33
 
9.6%
Other Punctuation 2
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 36
14.5%
a 30
12.1%
n 24
9.7%
r 20
8.1%
o 18
 
7.3%
i 18
 
7.3%
s 17
 
6.9%
l 15
 
6.0%
t 12
 
4.8%
y 10
 
4.0%
Other values (11) 48
19.4%
Uppercase Letter
ValueCountFrequency (%)
T 6
10.2%
J 6
10.2%
M 5
 
8.5%
G 5
 
8.5%
R 5
 
8.5%
D 5
 
8.5%
B 5
 
8.5%
S 4
 
6.8%
L 3
 
5.1%
H 2
 
3.4%
Other values (9) 13
22.0%
Space Separator
ValueCountFrequency (%)
32
97.0%
  1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 307
89.8%
Common 35
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 36
 
11.7%
a 30
 
9.8%
n 24
 
7.8%
r 20
 
6.5%
o 18
 
5.9%
i 18
 
5.9%
s 17
 
5.5%
l 15
 
4.9%
t 12
 
3.9%
y 10
 
3.3%
Other values (30) 107
34.9%
Common
ValueCountFrequency (%)
32
91.4%
& 2
 
5.7%
  1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341
99.7%
None 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 36
 
10.6%
32
 
9.4%
a 30
 
8.8%
n 24
 
7.0%
r 20
 
5.9%
o 18
 
5.3%
i 18
 
5.3%
s 17
 
5.0%
l 15
 
4.4%
t 12
 
3.5%
Other values (32) 119
34.9%
None
ValueCountFrequency (%)
  1
100.0%

generalmanager
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:08.160769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length11.74074074
Min length9

Characters and Unicode

Total characters317
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowTravis Schlenk
2nd rowBrad Stevens
3rd rowKoby Altman
4th rowTrajan Langdon
5th rowArturas Karnisovas
ValueCountFrequency (%)
travis 1
 
1.9%
bob 1
 
1.9%
stevens 1
 
1.9%
jon 1
 
1.9%
horst 1
 
1.9%
pat 1
 
1.9%
riley 1
 
1.9%
rob 1
 
1.9%
pelinka 1
 
1.9%
michael 1
 
1.9%
Other values (44) 44
81.5%
2023-07-13T22:07:08.393851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 30
 
9.5%
n 30
 
9.5%
27
 
8.5%
e 22
 
6.9%
i 22
 
6.9%
o 22
 
6.9%
r 19
 
6.0%
s 16
 
5.0%
t 13
 
4.1%
l 11
 
3.5%
Other values (29) 105
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 235
74.1%
Uppercase Letter 55
 
17.4%
Space Separator 27
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 30
12.8%
n 30
12.8%
e 22
9.4%
i 22
9.4%
o 22
9.4%
r 19
8.1%
s 16
 
6.8%
t 13
 
5.5%
l 11
 
4.7%
m 7
 
3.0%
Other values (11) 43
18.3%
Uppercase Letter
ValueCountFrequency (%)
S 6
10.9%
M 6
10.9%
J 6
10.9%
R 4
 
7.3%
P 4
 
7.3%
B 4
 
7.3%
K 4
 
7.3%
C 3
 
5.5%
T 3
 
5.5%
H 3
 
5.5%
Other values (7) 12
21.8%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 290
91.5%
Common 27
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 30
 
10.3%
n 30
 
10.3%
e 22
 
7.6%
i 22
 
7.6%
o 22
 
7.6%
r 19
 
6.6%
s 16
 
5.5%
t 13
 
4.5%
l 11
 
3.8%
m 7
 
2.4%
Other values (28) 98
33.8%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 30
 
9.5%
n 30
 
9.5%
27
 
8.5%
e 22
 
6.9%
i 22
 
6.9%
o 22
 
6.9%
r 19
 
6.0%
s 16
 
5.0%
t 13
 
4.1%
l 11
 
3.5%
Other values (29) 105
33.1%

headcoach
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:08.586097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length16
Median length14
Mean length12.25925926
Min length9

Characters and Unicode

Total characters331
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowQuin Snyder
2nd rowJoe Mazzulla
3rd rowJB Bickerstaff
4th rowWillie Green
5th rowBilly Donovan
ValueCountFrequency (%)
quin 1
 
1.9%
steve 1
 
1.9%
mazzulla 1
 
1.9%
adrian 1
 
1.9%
griffin 1
 
1.9%
erik 1
 
1.9%
spoelstra 1
 
1.9%
darvin 1
 
1.9%
ham 1
 
1.9%
tyronn 1
 
1.9%
Other values (44) 44
81.5%
2023-07-13T22:07:08.826738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 29
 
8.8%
27
 
8.2%
i 26
 
7.9%
e 24
 
7.3%
l 22
 
6.6%
n 21
 
6.3%
r 20
 
6.0%
o 17
 
5.1%
k 11
 
3.3%
u 10
 
3.0%
Other values (37) 124
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 249
75.2%
Uppercase Letter 55
 
16.6%
Space Separator 27
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 29
11.6%
i 26
10.4%
e 24
9.6%
l 22
 
8.8%
n 21
 
8.4%
r 20
 
8.0%
o 17
 
6.8%
k 11
 
4.4%
u 10
 
4.0%
s 10
 
4.0%
Other values (15) 59
23.7%
Uppercase Letter
ValueCountFrequency (%)
M 7
12.7%
J 6
 
10.9%
B 5
 
9.1%
D 4
 
7.3%
T 4
 
7.3%
C 3
 
5.5%
W 3
 
5.5%
S 3
 
5.5%
F 2
 
3.6%
G 2
 
3.6%
Other values (11) 16
29.1%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 304
91.8%
Common 27
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 29
 
9.5%
i 26
 
8.6%
e 24
 
7.9%
l 22
 
7.2%
n 21
 
6.9%
r 20
 
6.6%
o 17
 
5.6%
k 11
 
3.6%
u 10
 
3.3%
s 10
 
3.3%
Other values (36) 114
37.5%
Common
ValueCountFrequency (%)
27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 29
 
8.8%
27
 
8.2%
i 26
 
7.9%
e 24
 
7.3%
l 22
 
6.6%
n 21
 
6.3%
r 20
 
6.0%
o 17
 
5.1%
k 11
 
3.3%
u 10
 
3.0%
Other values (37) 124
37.5%

dleagueaffiliation
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:09.013376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length20
Mean length16.88888889
Min length11

Characters and Unicode

Total characters456
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)92.6%

Sample

1st rowCollege Park Skyhawks
2nd rowMaine Celtics
3rd rowCleveland Charge
4th rowBirmingham Squadron
5th rowWindy City Bulls
ValueCountFrequency (%)
city 4
 
5.5%
no 2
 
2.7%
affiliate 2
 
2.7%
blue 2
 
2.7%
clippers 1
 
1.4%
grande 1
 
1.4%
rio 1
 
1.4%
ontario 1
 
1.4%
of 1
 
1.4%
caliente 1
 
1.4%
Other values (57) 57
78.1%
2023-07-13T22:07:09.248419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
10.1%
a 39
 
8.6%
e 36
 
7.9%
i 28
 
6.1%
s 27
 
5.9%
l 25
 
5.5%
o 24
 
5.3%
t 24
 
5.3%
r 23
 
5.0%
n 21
 
4.6%
Other values (37) 163
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 336
73.7%
Uppercase Letter 71
 
15.6%
Space Separator 46
 
10.1%
Decimal Number 3
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39
11.6%
e 36
10.7%
i 28
 
8.3%
s 27
 
8.0%
l 25
 
7.4%
o 24
 
7.1%
t 24
 
7.1%
r 23
 
6.8%
n 21
 
6.2%
d 12
 
3.6%
Other values (14) 77
22.9%
Uppercase Letter
ValueCountFrequency (%)
C 13
18.3%
S 9
12.7%
W 6
 
8.5%
L 5
 
7.0%
M 5
 
7.0%
B 5
 
7.0%
A 4
 
5.6%
G 3
 
4.2%
N 3
 
4.2%
R 3
 
4.2%
Other values (9) 15
21.1%
Decimal Number
ValueCountFrequency (%)
9 1
33.3%
0 1
33.3%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 407
89.3%
Common 49
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39
 
9.6%
e 36
 
8.8%
i 28
 
6.9%
s 27
 
6.6%
l 25
 
6.1%
o 24
 
5.9%
t 24
 
5.9%
r 23
 
5.7%
n 21
 
5.2%
C 13
 
3.2%
Other values (33) 147
36.1%
Common
ValueCountFrequency (%)
46
93.9%
9 1
 
2.0%
0 1
 
2.0%
5 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
 
10.1%
a 39
 
8.6%
e 36
 
7.9%
i 28
 
6.1%
s 27
 
5.9%
l 25
 
5.5%
o 24
 
5.3%
t 24
 
5.3%
r 23
 
5.0%
n 21
 
4.6%
Other values (37) 163
35.7%

facebook
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:09.552538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length41
Median length38
Mean length35.74074074
Min length29

Characters and Unicode

Total characters965
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowhttps://www.facebook.com/hawks
2nd rowhttps://www.facebook.com/bostonceltics
3rd rowhttps://www.facebook.com/Cavs
4th rowhttps://www.facebook.com/PelicansNBA
5th rowhttps://www.facebook.com/chicagobulls
ValueCountFrequency (%)
https://www.facebook.com/hawks 1
 
3.7%
https://www.facebook.com/bostonceltics 1
 
3.7%
https://www.facebook.com/cavs 1
 
3.7%
https://www.facebook.com/pelicansnba 1
 
3.7%
https://www.facebook.com/chicagobulls 1
 
3.7%
https://www.facebook.com/dallasmavs 1
 
3.7%
https://www.facebook.com/denvernuggets 1
 
3.7%
https://www.facebook.com/warriors 1
 
3.7%
https://www.facebook.com/houstonrockets 1
 
3.7%
https://www.facebook.com/laclippers 1
 
3.7%
Other values (17) 17
63.0%
2023-07-13T22:07:09.910121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 100
 
10.4%
w 85
 
8.8%
/ 81
 
8.4%
t 69
 
7.2%
c 65
 
6.7%
s 59
 
6.1%
. 54
 
5.6%
e 51
 
5.3%
a 50
 
5.2%
k 35
 
3.6%
Other values (34) 316
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 772
80.0%
Other Punctuation 162
 
16.8%
Uppercase Letter 31
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 100
13.0%
w 85
11.0%
t 69
 
8.9%
c 65
 
8.4%
s 59
 
7.6%
e 51
 
6.6%
a 50
 
6.5%
k 35
 
4.5%
m 33
 
4.3%
h 33
 
4.3%
Other values (15) 192
24.9%
Uppercase Letter
ValueCountFrequency (%)
N 5
16.1%
M 4
12.9%
C 3
9.7%
T 3
9.7%
B 2
 
6.5%
O 2
 
6.5%
K 2
 
6.5%
A 2
 
6.5%
D 1
 
3.2%
L 1
 
3.2%
Other values (6) 6
19.4%
Other Punctuation
ValueCountFrequency (%)
/ 81
50.0%
. 54
33.3%
: 27
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 803
83.2%
Common 162
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 100
12.5%
w 85
 
10.6%
t 69
 
8.6%
c 65
 
8.1%
s 59
 
7.3%
e 51
 
6.4%
a 50
 
6.2%
k 35
 
4.4%
m 33
 
4.1%
h 33
 
4.1%
Other values (31) 223
27.8%
Common
ValueCountFrequency (%)
/ 81
50.0%
. 54
33.3%
: 27
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 100
 
10.4%
w 85
 
8.8%
/ 81
 
8.4%
t 69
 
7.2%
c 65
 
6.7%
s 59
 
6.1%
. 54
 
5.6%
e 51
 
5.3%
a 50
 
5.2%
k 35
 
3.6%
Other values (34) 316
32.7%

instagram
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:10.202631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length33
Mean length31.07407407
Min length26

Characters and Unicode

Total characters839
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowhttps://instagram.com/atlhawks
2nd rowhttps://instagram.com/celtics
3rd rowhttps://instagram.com/cavs
4th rowhttps://instagram.com/pelicansnba
5th rowhttps://instagram.com/chicagobulls
ValueCountFrequency (%)
https://instagram.com/atlhawks 1
 
3.7%
https://instagram.com/celtics 1
 
3.7%
https://instagram.com/cavs 1
 
3.7%
https://instagram.com/pelicansnba 1
 
3.7%
https://instagram.com/chicagobulls 1
 
3.7%
https://instagram.com/dallasmavs 1
 
3.7%
https://instagram.com/nuggets 1
 
3.7%
https://instagram.com/warriors 1
 
3.7%
https://instagram.com/houstonrockets 1
 
3.7%
https://instagram.com/laclippers 1
 
3.7%
Other values (17) 17
63.0%
2023-07-13T22:07:10.554705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 97
11.6%
s 82
 
9.8%
/ 81
 
9.7%
a 78
 
9.3%
m 62
 
7.4%
r 46
 
5.5%
i 43
 
5.1%
o 42
 
5.0%
c 41
 
4.9%
n 41
 
4.9%
Other values (17) 226
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 704
83.9%
Other Punctuation 135
 
16.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 97
13.8%
s 82
11.6%
a 78
11.1%
m 62
8.8%
r 46
 
6.5%
i 43
 
6.1%
o 42
 
6.0%
c 41
 
5.8%
n 41
 
5.8%
h 33
 
4.7%
Other values (14) 139
19.7%
Other Punctuation
ValueCountFrequency (%)
/ 81
60.0%
. 27
 
20.0%
: 27
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 704
83.9%
Common 135
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 97
13.8%
s 82
11.6%
a 78
11.1%
m 62
8.8%
r 46
 
6.5%
i 43
 
6.1%
o 42
 
6.0%
c 41
 
5.8%
n 41
 
5.8%
h 33
 
4.7%
Other values (14) 139
19.7%
Common
ValueCountFrequency (%)
/ 81
60.0%
. 27
 
20.0%
: 27
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 97
11.6%
s 82
 
9.8%
/ 81
 
9.7%
a 78
 
9.3%
m 62
 
7.4%
r 46
 
5.5%
i 43
 
5.1%
o 42
 
5.0%
c 41
 
4.9%
n 41
 
4.9%
Other values (17) 226
26.9%

twitter
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2023-07-13T22:07:10.886082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length31
Mean length29.07407407
Min length24

Characters and Unicode

Total characters785
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowhttps://twitter.com/ATLHawks
2nd rowhttps://twitter.com/celtics
3rd rowhttps://twitter.com/cavs
4th rowhttps://twitter.com/PelicansNBA
5th rowhttps://twitter.com/chicagobulls
ValueCountFrequency (%)
https://twitter.com/atlhawks 1
 
3.7%
https://twitter.com/celtics 1
 
3.7%
https://twitter.com/cavs 1
 
3.7%
https://twitter.com/pelicansnba 1
 
3.7%
https://twitter.com/chicagobulls 1
 
3.7%
https://twitter.com/dallasmavs 1
 
3.7%
https://twitter.com/nuggets 1
 
3.7%
https://twitter.com/warriors 1
 
3.7%
https://twitter.com/houstonrockets 1
 
3.7%
https://twitter.com/laclippers 1
 
3.7%
Other values (17) 17
63.0%
2023-07-13T22:07:11.279207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 148
18.9%
/ 81
 
10.3%
s 52
 
6.6%
r 44
 
5.6%
i 43
 
5.5%
e 43
 
5.5%
o 41
 
5.2%
c 40
 
5.1%
m 33
 
4.2%
h 30
 
3.8%
Other values (32) 230
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 617
78.6%
Other Punctuation 135
 
17.2%
Uppercase Letter 33
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 148
24.0%
s 52
 
8.4%
r 44
 
7.1%
i 43
 
7.0%
e 43
 
7.0%
o 41
 
6.6%
c 40
 
6.5%
m 33
 
5.3%
h 30
 
4.9%
p 30
 
4.9%
Other values (14) 113
18.3%
Uppercase Letter
ValueCountFrequency (%)
A 4
12.1%
H 3
9.1%
B 3
9.1%
T 3
9.1%
P 3
9.1%
L 3
9.1%
S 3
9.1%
M 2
 
6.1%
R 2
 
6.1%
N 2
 
6.1%
Other values (5) 5
15.2%
Other Punctuation
ValueCountFrequency (%)
/ 81
60.0%
: 27
 
20.0%
. 27
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 650
82.8%
Common 135
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 148
22.8%
s 52
 
8.0%
r 44
 
6.8%
i 43
 
6.6%
e 43
 
6.6%
o 41
 
6.3%
c 40
 
6.2%
m 33
 
5.1%
h 30
 
4.6%
p 30
 
4.6%
Other values (29) 146
22.5%
Common
ValueCountFrequency (%)
/ 81
60.0%
: 27
 
20.0%
. 27
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 148
18.9%
/ 81
 
10.3%
s 52
 
6.6%
r 44
 
5.6%
i 43
 
5.5%
e 43
 
5.5%
o 41
 
5.2%
c 40
 
5.1%
m 33
 
4.2%
h 30
 
3.8%
Other values (32) 230
29.3%